There is another model which interprets visual data to assist with lane-keeping, slow down or stop for pedestrians, inform the conductor of road signs... The final model combines all these inputs and incorporates the user preferences and then decides whether to brake or accelerate, how much to rotate the steering wheel.

Idk heh. The point of the high performance training is you can train the "conductor" role faster, and run inference faster. Assuming the car has limited compute/gpu resources, if you have a very high performance conductor function, you can dedicate that much more budget to visual/sensor inference and or any other models like the Trolley Problem decider (jk).

edit: grammar/details

I don't really disagree with anything you said, except to point out that a lot of the current crop of web devs have never experienced what it is to build an app without any abstraction such as React, and quite understandably have no idea what problems it is doing for them.

I do know that since hopping onto react with all of that ~baggage~ context, I have never wanted to program UIs with a different model. It is true that hooks introduce a layer of abstraction that is sometimes difficult to reason about, but IMO they boil down the problems we faced with class components/lifecycle/server rendering gotchas, and put them front and center - forcing you to confront and fix them rather than settling for a solution that works 99% of the time.

https://www.sciencealert.com/birds-see-magnetic-fields-crypt...

One last random association "I want to see Gamma Rays!" https://youtu.be/mPnx3zO3SDc?t=70

gp's premise about learning with the multi-particle landscape first sort of blew my mind.

I'm deep in the depths like OP and I can relate to this, like, a lot. Do you know of any literature or 'what to google for' regarding this sentiment/behavior?

If you run any of these systems in production, it is good to have a cursory understanding.